Search

With the recent developments on opening the terahertz (THz) spectrum for experimental purposes by the Federal Communications Commission, transceivers operating in the range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and high reliability THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering the huge bandwidth and the rapid changes in the characteristics of THz wireless channels, ray tracing and one-shot statistical modeling are not adequate to define an accurate channel model. In this work, we propose Gamma mixture based channel modeling for the THz band via the expectation-maximization (EM) algorithm. First, maximum likelihood estimation (MLE) is applied to characterize the Gamma mixture model parameters, and then EM algorithm is used to compute MLEs of the unknown parameters of the measurement data. The accuracy of the proposed model is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov (KS) test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. To efficiently evaluate the performance of the proposed method in more realistic scenarios, all the analysis is done by examining measurement data from a measurement campaign in the 240 GHz to 300 GHz frequency range, using a well-isolated anechoic chamber. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit to the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wide band, using the mixture models allows for determining channel parameters more precisely. [ABSTRACT FROM AUTHOR]

Modeling the variations in the local mean received power, the shadow fading is a relatively understudied effect in the literature. The inaccuracy of the universally accepted lognormal model is shown in many works. However, proposing other statistical distributions, such as gamma, which are not stemmed from the natural underlying physical process, cannot provide sufficient insights. Conceding the physical process of multiple reflections generating the lognormal distribution, in this paper, we propose a generalized mixture model that can address the modeling inaccuracies observed with a single lognormal distribution that may not correctly represent empirical data sets. To show that lognormal mixture model can be used under any shadow fading condition, we prove that an arbitrary probability density function can accurately be represented by a mixture of lognormal random variables (RVs). One of the main issues associated with mixture models is the determination of the mixture components. Here, we propose two solutions. Our first solution is a Dirichlet-process-mixture-based estimation technique that can provide the optimum number of components. Our second solution is an expectation–maximization (EM) algorithm-based technique for a more practical implementation. The proposed model and solution approaches are applied to our empirical data set, where the accuracy of the mixture model is verified by using both confidence-based and error-vector-norm-based techniques. Concluding this paper, we provide outage and cellular coverage probability expressions, where we show that more accurate shadow fading models yield more realistic performance estimates. [ABSTRACT FROM PUBLISHER]